On Learning Matrices with Orthogonal Columns or Disjoint Supports

  • Kevin Vervier
  • Pierre Mahé
  • Alexandre D’Aspremont
  • Jean-Baptiste Veyrieras
  • Jean-Philippe Vert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8726)


We investigate new matrix penalties to jointly learn linear models with orthogonality constraints, generalizing the work of Xiao et al. [24] who proposed a strictly convex matrix norm for orthogonal transfer. We show that this norm converges to a particular atomic norm when its convexity parameter decreases, leading to new algorithmic solutions to minimize it. We also investigate concave formulations of this norm, corresponding to more aggressive strategies to induce orthogonality, and show how these penalties can also be used to learn sparse models with disjoint supports.


Ridge Regression Orthogonal Matrice Nuclear Norm Sparse Model Disjoint Support 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Kevin Vervier
    • 1
    • 2
    • 3
  • Pierre Mahé
    • 1
  • Alexandre D’Aspremont
    • 4
  • Jean-Baptiste Veyrieras
    • 1
  • Jean-Philippe Vert
    • 2
    • 3
  1. 1.Data and Knowledge LabBiomerieuxMarcy l’EtoileFrance
  2. 2.Centre for Computational BiologyMines ParisTechFontainebleauFrance
  3. 3.Institut Curie, INSERM U900ParisFrance
  4. 4.CNRS and D.I. UMR 8548, Ecole normale supérieureParisFrance

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